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Evidence-Based Complementary and Alternative Medicine
Volume 2012 (2012), Article ID 516473, 5 pages
http://dx.doi.org/10.1155/2012/516473
Research Article

Visual Agreement Analyses of Traditional Chinese Medicine: A Multiple-Dimensional Scaling Approach

1Department of Traditional Chinese Medicine, Changhua Christian Hospital, Changhua 50006, Taiwan
2Graduate Institute of Statistics and Information Science, National Changhua University of Education, Changhua 50058, Taiwan
3Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan

Received 4 July 2012; Revised 9 August 2012; Accepted 17 August 2012

Academic Editor: Zhaoxiang Bian

Copyright © 2012 Lun-Chien Lo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The study of TCM agreement in terms of a powerful statistical tool becomes critical in providing objective evaluations. Several previous studies have conducted on the issue of consistency of TCM, and the results have indicated that agreements are low. Traditional agreement measures only provide a single value which is not sufficient to justify if the agreement among several raters is strong or not. In light of this observation, a novel visual agreement analysis for TCM via multiple dimensional scaling (MDS) is proposed in this study. If there are clusters present in the raters in a latent manner, MDS can prove itself as an effective distinguisher. In this study, a group of doctors, consisting of 11 experienced TCM practitioners having clinical experience ranging from 3 to 15 years with a mean of 5.5 years from the Chinese Medicine Department at Changhua Christian Hospital (CCH) in Taiwan were asked to diagnose a total of fifteen tongue images, the Eight Principles derived from the TCM theorem. The results of statistical analysis show that, if there are clusters present in the raters in a latent manner, MDS can prove itself as an effective distinguisher.